The number is only useful when the timer boundary is visible.
Burst Load source-to-target speed, CDC parser speed, durable capture speed, and target apply answer different questions. This page keeps the measurements separate, names the workload behind each one, and shows what must still happen after the timer stops.
Split tables and independent ranges across workers so migrations can use the source, network, and target capacity available to them.
Read the database’s native log and preserve a durable handoff before delivery, so downstream work can catch up without losing the stream.
Combine the initial load and live changes while the source stays available, then switch over from a known, validated position.
Coordinate delivery with row shape, indexes, network limits, and target behavior instead of optimizing an isolated reader number.
Retain completed slices and retry unfinished work instead of restarting an entire load.
Before you compare two numbers
Find the first component the benchmark did not measure.
Burst Load source → target
Times the complete table-movement path. Source read shape, row width, conversion, network, prepared files, and target ingestion all contribute.
Reader or decoder
Times byte scanning, parsing, or row decoding in isolation. Useful for finding CPU ceilings; incomplete as a pipeline promise.
Source → durable spool
Includes capture, event materialization, zstd compression, and durable spool writes with fsync. It still stops before a destination applies the change.
End-to-end apply
Starts at the source and stops only after the target has applied the change. Row shape, DML mix, target indexes, network, and apply strategy all matter.
Production performance is a system property.
SQL Flow improves useful throughput by combining parallel table movement, native log capture, durable handoff, and target-aware delivery. The result is a migration and replication path that keeps working when source and destination workloads do not move at the same pace.
The right design still depends on row widths, transaction sizes, insert/update/delete mix, network, and target indexes. SQL Flow absorbs differences in pace and preserves a checkpointed handoff; it does not pretend a fast source can make a constrained destination consume faster than it allows.
The benchmark that matters uses your rows.
Bring a representative schema, DML mix, source mode, and destination. We will agree on the timer boundary first, then measure capture and end-to-end apply without substituting an easier workload.
SQL Flow handles automatically
The software does the migration work.
- SQL Flow measures full load, capture, durable transfer, and target apply separately.
- Parallel work is balanced automatically across useful capacity.
- The limiting stage remains visible instead of being hidden in one aggregate rate.
You choose
Keep control of the decisions that matter.
- Choose maximum throughput or bounded production impact.
- Set worker, bandwidth, memory, and batching limits.
- Choose which stage or table to optimize first.